Senior AI/ML Engineer

SourcingXPress; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

RapidClaims is a leader in AI-driven revenue cycle management, transforming how US healthcare providers run mid- and end-revenue cycle operations—from coding and claim edits to denials triage and appeal generation.

We’re hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You’ll build scalable, auditable production systems that automate coding and payer-rule reasoning in a regulated environment.

Qualifications

• 5+ years in ML/AI engineering with 6+ months in production LLM systems.
• Hands‑on experience deploying and operating self-hosted LLMs (vLLM, SGLang, TensorRT-LLM).
• Strong Python + PyTorch + Hugging Face experience.
• Designing embedding-based retrieval and/or knowledge graphs for grounded LLM apps.
• Proven ownership of evaluation infrastructure—offline benchmarks, online monitoring, drift and regression detection.

Responsibilities

• Self-Hosted LLM Infrastructure.
• Deploy, fine-tune, and operate open-source models as our primary inference stack.
• Work with vLLM / SGLang / TensorRT-LLM for serving at scale.
• Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO).
• Optimize GPU usage, latency, batching, and cost; explicit build-vs-buy trade-offs.
• Knowledge Graphs & Embedding-Based Retrieval — design and maintain systems grounding outputs.
• Build embedding-based retrieval over clinical notes, claims, denial reasons, and policies.
• Combine graph traversal and dense retrieval for auditable grounding.
• Ingestion, versioning, and quality of knowledge sources (CMS, AHA, AMA, NCCI).
• Evaluation & Monitoring — gate model/prompts/retrieval/graph changes before production.

Skills

5+ years ML/AI engineering

Production LLM systems

Self-hosted LLM deployment

Python + PyTorch + Hugging Face

Embedding-based retrieval

Knowledge graphs

Evaluation infrastructure

Gatekeeping/in offline evals

MCP/tool orchestration

Cost/latency optimization

Tools

vLLM

SGLang

TensorRT-LLM

Langfuse

LangSmith

Arize

Job description

Job Description

Company: RapidClaims
LinkedIn: Visit LinkedIn
Business Type: Startup
Company Type: Product
Business Model: B2B
Funding Stage: Series A
Industry: Healthcare
Salary Range: ₹ 30-50 Lacs PA

Job Description

RapidClaims is a leader in AI-driven revenue cycle management, transforming how US healthcare providers run mid- and end-revenue cycle operations — from medical coding and charge capture through claim scrubbing, denials management, appeals, and payment posting.

The company has raised $11 million in total funding from top investors, including Accel and Together Fund.

Join us as we scale a cloud-native platform that runs self-hosted, fine-tuned Large Language Models, knowledge graphs, and embedding-based retrieval over millions of clinical notes, claims, and payer-policy documents every month. You’ll engineer autonomous pipelines that parse clinical records and translate into codes, provide documentation improvement parameters, and even solve for denials with autonomous calling if needed; Tackle the deep-domain challenges that make clinical and RCM AI one of the most rewarding problems in tech.

Senior AI/ML Engineer- Job Overview

We are hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You will build production systems that automate mid- and end-revenue cycle workflows for US healthcare spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning.

This is a production engineering role (not research) focused on building scalable, auditable, and cost-efficient LLM systems in a regulated healthcare environment.

What You’ll Own

• Self-Hosted LLM Infrastructure
• Deploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and successors) as our primary inference stack
• Work with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined attention to throughput, tail latency, batching, KV-cache, and GPU economics
• Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data
• Optimize GPU usage, latency, batching, and cost; make build-vs-buy and hosted-vs-self-hosted trade-offs explicit and measured
• Knowledge Graphs & Embedding-Based Retrieval
• Design and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules — and the relationships between them
• Build embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora — including chunking, embedding model selection, hybrid search, and re ranking
• Combine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence
• Own ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins)
• Evaluation & Monitoring
• Build continuous evaluation pipelines that gate every model, prompt, retrieval, and graph change before production
• Run offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth
• Monitor drift, hallucinations, regressions, and output quality in production; operate shadow-mode rollouts and per-cohort accuracy tracking (specialty, payer, chart type)
• Track business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency
• LLM Systems & Prompt Engineering
• Design prompts and context pipelines for coding (CPT, ICD, HCC, E/M), claim edits, denial classification, and appeal drafting
• Implement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack
• Apply RAG over medical coding standards (CMS, ICD-10, AHA, NCCI) and payer policies, grounded in the knowledge graph and embedding stores
• Treat prompts as a thin, well-versioned, well-evaluated layer — never the load-bearing piece
• Agentic Workflows & Tooling — MCP
• Build MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic, eligibility, denial classification
• Design multi-step agent workflows with audit trails and human-in-the-loop checkpoints for coder, biller, and AR-analyst review
• Define deterministic vs. LLM-based tool boundaries for reliability — reliability comes from knowing which is which

What We’re Looking For

Must‑Have

• 5+ years in ML/AI engineering, including 6+ months in production LLM systems
• Hands‑on experience deploying and operating self‑hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent)
• Strong experience designing embedding-based retrieval and/or knowledge graphs for grounded LLM applications
• Demonstrated ownership of evaluation infrastructure — offline benchmarks, online monitoring, drift and regression detection
• Strong Python + PyTorch + Hugging Face experience
• Production experience with monitoring, incidents, and system ownership

Strongly Preferred

• Fine‑tuning experience (SFT, LoRA, QLoRA, DPO) on domain‑specific corpora
• Experience with graph databases (Neo4j, ArangoDB, or equivalent) and graph‑aware retrieval
• Experience with vector databases and hybrid search (BM25 + dense, rerankers)
• Familiarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or in‑house equivalents)
• Exposure to healthcare, RCM, claims, or other regulated domains
• Experience with MCP or similar tool‑orchestration frameworks
• Strong prompt‑engineering and LLM‑evaluation instincts

What We Offer

• Work on high‑impact healthcare AI systems used in real billing and RCM workflows
• Ownership of production LLM, retrieval, and evaluation systems end‑to‑end
• Solve real‑world problems with real constraints (cost, latency, compliance, auditability)